Development of prediction models for complications after primary total hip and knee arthroplasty: a single-centre retrospective cohort study in the Netherlands.

OBJECTIVE

The aim of this study was to develop prediction models for patients with total hip arthroplasty (THA) and total knee arthroplasty (TKA) to predict the risk for surgical complications based on personal factors, comorbidities and medication use.

DESIGN

Retrospective cohort study.

SETTING

Tertiary care in outpatient clinic of university medical centre.

PARTICIPANTS

3776 patients with a primary THA or TKA between 2004 and 2018.

PRIMARY AND SECONDARY OUTCOME MEASURES: Multivariable logistic regression models were developed for primary outcome surgical site infection (SSI), and secondary outcomes venous thromboembolism (VTE), postoperative bleeding (POB), luxation, delirium and nerve damage (NER).

RESULTS

For SSI, age, smoking status, body mass index, presence of immunological disorder, diabetes mellitus, liver disease and use of non-steroidal anti-inflammatory drugs were included. An area under the receiver operating characteristic curve (AUC) of 71.9% (95% CI=69.4% to 74.4%) was found. For this model, liver disease showed to be the strongest predictor with an OR of 10.7 (95% CI=2.4 to 46.6). The models for POB and NER showed AUCs of 73.0% (95% CI=70.7% to 75.4%) and 76.6% (95% CI=73.2% to 80.0%), respectively. For delirium an AUC of 85.9% (95% CI=83.8% to 87.9%) was found, and for the predictive algorithms for luxation and VTE we found least favourable results (AUC=58.4% (95% CI=55.0% to 61.8%) and AUC=66.3% (95% CI=62.7% to 69.9%)).

CONCLUSIONS

Discriminative ability was reasonable for SSI and predicted probabilities ranged from 0.01% to 51.0%. We expect this to enhance shared decision-making in considering THA or TKA since current counselling is predicated on population-based probability of risk, rather than using personalised prediction. We consider our models for SSI, delirium and NER appropriate for clinical use when taking underestimation and overestimation of predicted risk into account. For VTE and POB, caution concerning overestimation exceeding a predicted probability of 0.08 for VTE and 0.05 for POB should be taken into account. Furthermore, future studies should evaluate clinical impact and whether the models are feasible in an external population.